Week 43: The Latest on HRV-Guided Training
Hi there 👋
I hope you are doing well.
This week, we talk about HRV-guided training, as I was recently reading Carla Alfonso’s study, “Individual training prescribed by heart rate variability, heart rate and well-being scores in experienced cyclists“ which you can find here.
I met Carla a few years ago, and some of you might have even participated in this remote study, as we advertised in a past HRV4Training newsletter. The study is an important milestone because it experimentally tested the kind of HRV-guided decision framework that I’ve been writing about for years, combining deviations from your normal range and daily subjective assessments for additional context to adjust training intensity dynamically (which is exactly what the app HRV4Training does). On top of that, Carla also looked at adding resting heart rate to the picture, something I covered more this year when discussing normalized HRV - with promising results.
The protocol used was similar to the one originally introduced by Kiviniemi (see the origins of HRV-guided training, here). After a short baseline period to define each athlete’s normal range, participants received daily guidance (train hard, go easy, or rest) based on how their morning HRV compared to that normal range. Carla’s team also tested versions that combined HRV with subjective well-being (and where the subjective data would override HRV, again as we do it in HRV4Training), and a third protocol that added also resting heart rate, which accounts for the confounding influence of resting heart rate on HRV (a stable HRV with a stable resting heart rate might not be the same as a stable HRV with a suppressed resting heart rate, for example).
This last protocol is something I’ve emphasized this year in my writing and that we’ve implemented in HRV4Training Pro, where it’s available as Normalized HRV (HRV:HR) in the Overview page. Normalization makes HRV interpretation more robust, since heart rate and HRV are tightly linked, and it prevents misclassifying normal physiological changes as signs of fatigue (or the other way around), as well as in the context of parasympathetic saturation. This is why I always keep an eye on Normalized HRV as well for the athletes I coach, using HRV4Training Pro and the Coach Panel.
How did the study go? The results were encouraging. All groups improved, but the one using HRV combined with resting heart rate plus well-being showed the greatest overall gains and the most consistent progress.
Needless to say, combining objective physiology with subjective feedback leads to better decision-making and more effective training adaptation. For me, this work validates what many of us have experienced for years: HRV can be a useful daily guide, but only when interpreted in context (e.g. subjective data) and anchored to your own physiology (i.e. normal range, changes in resting heart rate).
I’m grateful to everyone who contributed to this research by taking part through our newsletter. Thank you!
Additional reads:
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When using Pro, the app will also automatically recognize your account and add the Normal Range to the Baseline view, together with detected trends and additional annotations, which can help contextualizing longer-term changes.
You will also be able to pick rMSSD as the parameter to see on the homepage of the app.
See you next week!
Marco holds a PhD cum laude in applied machine learning, a M.Sc. cum laude in computer science engineering, and a M.Sc. cum laude in human movement sciences and high-performance coaching. He is a certified Ultrarunning Coach.
Marco has published more than 50 papers and patents at the intersection between physiology, health, technology, and human performance.
He is co-founder of HRV4Training, Endurance Coach at Destination Unknown, advisor at Oura, guest lecturer at VU Amsterdam, and editor for IEEE Pervasive Computing Magazine. He loves running.
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